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Results for “deconvolution”

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PubMed1995

Deconvolution of fully overlapped reflections from crystals of foot-and-mouth disease virus O1 G67.

Foot-and-mouth disease virus O(1) G67 forms crystals that appear similar to those of the closely related viruses O(1)K and O(1)BFS, both of which belong to space group I23. Statistical disorder in the O(1) G67 crystals means, however, that the measured diffraction data possess higher symmetry consistent with point group 432. It is shown that this is due to intimate twinning, with mosaic blocks randomly distributed between the two orientations. This results in a twofold loss of information due to the exact superimposition of non-identical reflections from the two orientations. A simple procedure has been devised to deconvolute these overlapped reflections by applying constraints in both real and reciprocal space. This procedure works well, providing interpretable electron-density maps for this virus. Other applications are discussed.

Journal Article↗

PubMed2000

Deconvoluting ultrafast structural dynamics: temporal resolution beyond the pulse length of synchrotron radiation.

100 picosecond X-ray snapshots visualizing the structural dynamics of macromolecular systems are now routinely available at synchrotron sources. A wealth of fundamental processes in photochemistry, condensed matter physics and biology, however, occur on considerably faster time scales. Standard experimental protocols at synchrotron sources cannot provide structural information with faster temporal resolution as these are limited by the duration of the electron bunch within the synchrotron ring. By walking the timing of femtosecond laser photolysis through a (much longer) X-ray pulse in steps of a few picoseconds, structural information on ultrafast dynamics may be retrieved from a set of X-ray scattering images, initially through deconvolution and subsequently through refinement. This experimental protocol promises immediate improvements in the temporal resolution available at synchrotron sources, facilitating the study of a number of rapid complex photochemical processes. Combined with techniques which reshape the X-ray probe pulse, the accessible temporal domain could further be extended to near-picosecond resolution.

Journal Article↗

PubMed1991

Deconvolution of tracer and dilution data using the Wiener filter.

In the study of living systems it is often necessary to inject or infuse a substance into the peripheral circulation and monitor its subsequent concentration in the plasma with time. Examples abound in the pharmacokinetic study of drugs and in the use of the indicator dilution technique for measuring blood flow. Furthermore, it is often necessary to deconvolve one such measured, and hence noisy, data set with another. One of the standard methods for deconvolving noisy signals is the Wiener filter, which is generally derived as a real window in the frequency domain such that the mean squared error between the estimated deconvolved function and the truth, on average, is minimized. Application of the Wiener filter requires some (often crude) model of the noise-to-signal power ratio as a function of frequency. In the pharmacokinetic and indicator dilution situations, however, one invariably has a good model of the actual function to be deconvolved in the form of a sum of decaying exponential functions. Such a model may be employed to calculate the signal-to-noise power ratio for use in the Wiener filter, or alternatively may be directly deconvolved itself. It is shown that better results are achieved with the Wiener filter if the model of the signal is not particularly accurate, whereas with a very accurate model it is better to deconvolve the model itself. The point at which the two deconvolution approaches perform comparably occurs when the error in the model is of a similar magnitude to the noise.

Mathematical Computing↗

PubMed1993

Linear and nonlinear techniques for the deconvolution of hormone time-series.

Pulsatile hormone secretion is usually investigated by measuring hormone concentration in samples of peripheral plasma. In this paper, the deconvolution of hormone time-series to reconstruct the instantaneous secretion rate of glands is considered. Various techniques are discussed and compared in order to overcome the ill-conditioning of the problem and reduce the computational burden. In particular, linear techniques based on least squares, maximum a posteriori (MAP) estimation, and Wiener filtering are compared. A new nonlinear MAP estimator that keeps into account the non-Gaussian distribution of the unknown signal is worked out and shown to yield the best results. The performances of the algorithms are tested on simulated time-series as well as on series of Luteinizing Hormone (LH).

Algorithms↗

PubMed1994

An automated film reader for DNA sequencing based on homomorphic deconvolution.

An automated reader for electrophoresis based DNA sequencing methods is described that provides fast and accurate sequence determination. Digitized sequencing lanes are processed with homomorphic blind deconvolution in preparation for peak detection, interlane alignment, peak refinement and base calling. Initial reads from direct blot sequencing films have error rates of about 1% at the rate of 5 nucleotides/s. Typical read lengths are 500-600 nucleotides. The described reader is a significant improvement over existing readers and could be an essential component in the sequencing efforts of the Human Genome Project.

Base Sequence↗

PubMed1995

Deconvolution of infrequently sampled data for the estimation of growth hormone secretion.

In this paper, the deconvolution of infrequently and nonuniformly sampled data is addressed. A nonparametric technique is worked out that provides a smooth estimate of the unknown input signal and takes into account nonnegativity constraints. In spite of the size of the problem, efficient algorithms for solving the constrained optimization problem and computing confidence intervals are proposed. The new technique is used to estimate growth hormone (GH) secretion after repeated GH-releasing hormone (GHRH) administration from samples of blood concentration.

Adult↗

PubMed1991

A deconvolution technique for improved estimation of rapid changes in ion concentration recorded with ion-selective microelectrodes.

In biological preparations, measurements of rapid, stimulus-evoked changes in ion concentration by ion-selective microelectrodes can be distorted by the limited bandwidth of these sensors. Techniques were developed to reconstruct the actual change in ion concentration using deconvolution of the electrode's output signal and the electrode's transfer function. In the vertebrate retina, a knowledge of the actual time course of a light-evoked increase in extracellular K+ concentration was used to provide a rigorous test of a hypothesis regarding the electrical origin of a clinically important component of the electroretinogram.

Animals↗

PubMed2001

Three-dimensional blind deconvolution of ultrasound images.

Three-dimensional ultrasound images are blurred by the ultrasound pulse through the convolution between the 3-D tissue signal and the 3-D pulse. The blurring reduces the spatial resolution of the 3-D ultrasound images and, consequently, their diagnostic value. This paper presents a method for 3-D blind homomorphic deconvolution of medical 3-D ultrasound images to improve their spatial resolution. The blind estimate of the 3-D pulse is necessary because the pulse changes in spatial extent and frequency composition as it passes through the tissues and because the pulse is not separable in its spatial dimensions. The method was tested on a 3-D image of a phantom with anechoic spheres of known size in a uniform diffuse scattering matrix. The spheres were clearly better defined and had volumes much closer to the true volume in the deconvolved image than in the original image.

Biomedical Engineering↗

PubMed2006

Constrained least squares filtering algorithm for ultrasound image deconvolution.

A new medical ultrasound tissue model is considered in this paper, which incorporates random fluctuations of the tissue response and provides more realistic interpretation of the received pulse-echo ultrasound signal. Using this new model, we propose an algorithm for restoration of the degraded ultrasound image. The proposed deconvolution is a modification of the classical regularization technique which combines Wiener filter and the constrained least squares (LS) algorithm for restoration of the ultrasound image. The performance of the algorithm is evaluated based on both the simulated phantom images and real ultrasound radio frequency (RF) data. The results show that the algorithm can provide improved ultrasound imaging performance in terms of the resolution gain. The deconvolved images visually show better resolved tissue structures and reduce speckle, which are confirmed by a medical expert.

Algorithms↗

PubMed2006

Bayesian wavelet-based image deconvolution: a GEM algorithm exploiting a class of heavy-tailed priors.

Image deconvolution is formulated in the wavelet domain under the Bayesian framework. The well-known sparsity of the wavelet coefficients of real-world images is modeled by heavy-tailed priors belonging to the Gaussian scale mixture (GSM) class; i.e., priors given by a linear (finite of infinite) combination of Gaussian densities. This class includes, among others, the generalized Gaussian, the Jeffreys, and the Gaussian mixture priors. Necessary and sufficient conditions are stated under which the prior induced by a thresholding/shrinking denoising rule is a GSM. This result is then used to show that the prior induced by the "nonnegative garrote" thresholding/shrinking rule, herein termed the garrote prior, is a GSM. To compute the maximum a posteriori estimate, we propose a new generalized expectation maximization (GEM) algorithm, where the missing variables are the scale factors of the GSM densities. The maximization step of the underlying expectation maximization algorithm is replaced with a linear stationary second-order iterative method. The result is a GEM algorithm of O(N log N) computational complexity. In a series of benchmark tests, the proposed approach outperforms or performs similarly to state-of-the art methods, demanding comparable (in some cases, much less) computational complexity.

Algorithms↗

PubMed2004

Phase unwrapping for 2-D blind deconvolution of ultrasound images.

In most approaches to the problem of two-dimensional homomorphic deconvolution of ultrasound images, the estimation of a corresponding point-spread function (PSF) is necessarily the first stage in the process of image restoration. This estimation is usually performed in the Fourier domain by either successive or simultaneous estimation of the amplitude and phase of the Fourier transform (FT) of the PSE This paper addresses the problem of recovering the FT-phase of the PSF, which is an important reconstruction problem by itself. The purpose of this paper is twofold. First, it provides a theoretical framework, establishing that the FT-phase of the PSF can be effectively estimated by a proper smoothing of the FT-phase of the appropriate radio-frequency (RF) image. Second, it presents a novel approach to the estimation of the FT-phase of the PSF, by solving a continuous Poisson equation over a predefined smooth subspace, in contrast to the discrete Poisson equation solver used for the classical least mean squares phase unwrapping algorithms, followed by a smoothing procedure. The proposed approach is possible due to the distinct properties of the FT-phases, among which the most important property is the availability of precise values of their partial derivatives. This property overcomes the main disadvantage of the discrete schemes, which routinely use wrapped (principal) values of the phase in order to approximate its partial derivatives. Since such an approximation is feasible subject to the restriction that the partial phase differences do not exceed pi in absolute value, the discrete schemes perform satisfactory only for few practical situations. The proposed approach is shown to be independent of this restriction and, thus, it performs for a wider class of the phases with significantly lower errors. The main advantages of the novel method over the algorithms based on discrete schemes are demonstrated in a series of computer simulations and for in vivo measurements.

Algorithms↗

PubMed2006

MLEM deconvolution of protein X-ray diffraction images based on a multiple-PSF model.

In this paper we analyze the degradation of protein X-ray diffraction images by diffuse light distortion (DLD). In order to correct the degradation, a new multiple point spread function (PSF) model is introduced and used to restore X-ray diffraction image data (XRD). Raw PSFs are collected from isolated spots in high-resolution areas on the diffraction patterns which represent the orientation of DLDs. An adaptive ridge regression (ARR) technique is used to remove noise from the raw PSF data. A target Gaussian function is used to model the raw PSFs. A maximum likelihood expectation maximization (MLEM) algorithm combined with a multi-PSF model is employed to restore high intensity, asymmetrical protein X-ray diffraction data. Experimental results using a single and multiple PSFs are presented and discussed. We show that using a multiple PSF model in the deconvolution algorithm improved the quality of the XRD and as a result the spot integration error (chi-squared) and corresponding electron density mapare improved.

Algorithms↗

PubMed2006

Reduced complexity rotation invariant texture classification using a blind deconvolution approach.

In this paper, we present a texture classification procedure that makes use of a blind deconvolution approach. Specifically, the texture is modeled as the output of a linear system driven by a binary excitation. We show that features computed from one-dimensional slices extracted from the two-dimensional autocorrelation function (ACF) of the binary excitation allows representing the texture for rotation-invariant classification purposes. The two-dimensional classification problem is thus reconduced to a more simple one-dimensional one, which leads to a significant reduction of the classification procedure computational complexity.

Algorithms↗

PubMed1993

Absorption and in vivo dissolution of hydroxycholoroquine in fed subjects assessed using deconvolution techniques.

1. Nine healthy subjects each received three doses of 155 mg rac-hydroxychloroquine, as a tablet, an oral solution and by intravenous infusion, in a randomised cross-over design study, 30 min after a standard high fat breakfast. 2. Four methods of deconvolution were used to assess the absolute bioavailability of the tablet and oral solution doses. These were the delta function method, the staircase approximation method, and two least squares methods using a single first-order input and a sequential first-order input. The mean (+/- s.d.) fraction absorbed estimated by the four methods was 0.64 +/- 0.14 after the tablet and 0.87 +/- 0.30 after the oral solution. Wide intersubject variability was observed (0.50-0.91 for the tablet; 0.30-1.37 for the solution). 3. The mean (+/- s.d.) absorption half-life was 3.7 +/- 2.0 h for the tablet and 3.3 +/- 1.6 h for the solution, suggesting that absorption following the tablet dose was not rate-limited by dissolution. 4. The in vivo dissolution rate, extent of release and lag-time were determined using cube-root law and first-order input functions. Dissolution was found to be rapid, after a significant lag-time, but incomplete in some subjects. 5. The rate and extent of absorption was similar to that reported previously for fasted subjects. The lag-time before absorption commenced in fed subjects (1.65 +/- 0.46 h) showed a significant three-fold increase over that reported previously in fasting subjects (0.63 +/- 0.33 h), but this difference is not likely to be of clinical significance.

Administration, Oral↗

PubMed1990

The application of deconvolution analysis to elucidate the pulsatile nature of growth hormone secretion using a variable half-life of growth hormone.

A deconvolution analysis model to calculate pituitary growth hormone (GH) secretion rate from measured serum GH concentration has been developed. This uses an iterative method of 'curve-stripping' based on an estimate of the half-life. The model has been applied to serum GH profiles and demonstrates that GH secretion occurs in discrete bursts with quiescent periods between secretory episodes, an 'on-off' phenomenon. The model can clearly dissect complicated concentration profiles such as the serum GH concentration response to growth hormone releasing hormone. The estimate was derived from calculating the half-life of serum GH in 10 subjects following an intravenous bolus injection of 50 mU of biosynthetic human growth hormone (b-hGH) and following infusions of the exogenous hormone (3 mU/kg/h) for 15, 30, 60 and 180 min. Endogenous GH secretion was suppressed by a continuous infusion of somatostatin (1-14). An asymptotic relationship between the duration of GH infusion and the GH half-life was established. A half-life of 15.3 min was achieved after exposure to GH for 60 min and a maximum half-life of 15.7 min after 180 min exposure.

Adolescent↗

PubMed2006

Least-square deconvolution: a framework for interpreting short tandem repeat mixtures.

Interpreting mixture short tandem repeat DNA data is often a laborious process, involving trying different genotype combinations mixed at assumed DNA mass proportions, and assessing whether the resultant is supported well by the relative peak-height information of the mixture sample. If a clear pattern of major-minor alleles is apparent, it is feasible to identify the major alleles of each locus and form a composite genotype profile for the major contributor. When alleles are shared between the two contributors, and/or heterozygous peak imbalance is present, it becomes complex and difficult to deduce the profile of the minor contributor. The manual trial and error procedures performed by an analyst in the attempt to resolve mixture samples have been formalized in the least-square deconvolution (LSD) framework reported here for two-person mixtures, with the allele peak height (or area) information as its only input. LSD operates on the peak-data information of each locus separately, independent of all other loci, and finds the best-fit DNA mass proportions and calculates error residual for each possible genotype combination. The LSD mathematical result for all loci is then to be reviewed by a DNA analyst, who will apply a set of heuristic interpretation guidelines in an attempt to form a composite DNA profile for each of the two contributors. Both simulated and forensic peak-height data were used to support this approach. A set of heuristic guidelines is to be used in forming a composite profile for each of the mixture contributors in analyzing the mathematical results of LSD. The heuristic rules involve the checking of consistency of the best-fit mass proportion ratios for the top-ranked genotype combination case among all four- and three-allele loci, and involve assessing the degree of fit of the top-ranked case relative to the fit of the second-ranked case. A different set of guidelines is used in reviewing and analyzing the LSD mathematical results for two-allele loci. Resolution of two-allele loci is performed with less confidence than for four- and three-allele loci. This paper gives a detailed description of the theory of the LSD methodology, discusses its limitations, and the heuristic guidelines in analyzing the LSD mathematical results. A 13-loci sample case study is included. The use of the interpretation guidelines in forming composite profiles for each of the two contributors is illustrated. Application of LSD in this case produced correct resolutions at all loci. Information on obtaining access to the LSD software is also given in the paper.

Algorithms↗

PubMed1998

The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of some Eubacterium species.

Pyrolysis mass spectrometry was used to produce complex biochemical fingerprints of Eubacterium exiguum, E. infirmum, E. tardum and E. timidum. To examine the relationship between these organisms the spectra were clustered by canonical variates analysis, and four clusters, one for each species, were observed. In an earlier study we trained artificial neural networks to identify these clinical isolates successfully; however, the information used by the neural network was not accessible from this so-called 'black box' technique. To allow the deconvolution of such complex spectra (in terms of which masses were important for discrimination) it was necessary to develop a system that itself produces 'rules' that are readily comprehensible. We here exploit the evolutionary computational technique of genetic programming; this rapidly and automatically produced simple mathematical functions that were also able to classify organisms to each of the four bacterial groups correctly and unambiguously. Since the rules used only a very limited set of masses, from a search space some 50 orders of magnitude greater than the dimensionality actually necessary, visual discrimination of the organisms on the basis of these spectral masses alone was also then possible.

Artificial Intelligence↗

PubMed1989

Determination of microscopic dissociation constants of 3-hydroxy-alpha-(methylamino)methyl-benzenemethanol by a spectral deconvolution method.

Microscopic dissociation constants of 3-hydroxy-alpha-(methylamino)methyl-benzenemethanol have been calculated from the titration spectrophotomeric data (c = 3.8 x 10(-4) M. Ionic strength = 0.16; buffer system: H3BO3/KOH) by application of a spectral deconvolution method. The results found (pKa = 9.48; pKb = 9.71; pKc = 10.12 and pKd = 9.88) are in good concordance with those obtained from the conventional regression linear method (pKa = 9.45; pKb = 9.77; pKc = 10.14 and pKd = 9.81).

Chemical Phenomena↗